Blind identification of signal modulations is a difficult task that bridges signal detection and the creation of useful information from received signals. This task is even more challenging in a non-cooperative or noisy environment with realistic channel properties, even with prior knowledge of the modulations to be detected. When such information is not available, classification is generally not feasible as most existing methods require prior information regarding the modulation mechanism.
Broadly, automatic modulation classification (AMC) techniques fall into two categories: likelihood-based (LB) and feature-based (FB). LB classification methodology is shown in FIG. 1A, and FB classification methodology is shown in FIG. 1B.
In the LB AMC methodology shown in FIG. 1A, modulation design knowledge 105 is used, along with signal models 130, noise models 120, and a cost function 140, to create a likelihood function of the input signals 150 belonging to a modulation. The likelihood function, in turn, is used to create a likelihood ratio, which is compared to a pre-determined decision threshold in a likelihood test 160. The output 170 is an indication as to which modulation type the input signal belongs to. LB AMC is optimal from a theoretical Bayesian perspective, in that it minimizes the chance of a wrong classification. However, LB AMC has high computational complexity and requires careful design and selection of signal and noise models
The FB AMC methodology shown in FIG. 1B uses expert-selected or designed signal features 135 based on known modulation characteristics 110 of expected modulations in a decision tree 165 with associated defined thresholds 145 to determine an output 175 of the modulation type of the input signals 150.
Conventional AMC methods, like the LB AMC method and the FB AMC method, require substantial design-side knowledge about the modulation properties and make specific assumptions regarding environmental noise. This requirement of advance expert knowledge makes these methods complex and largely unusable in noisy or uncooperative environments.
An AMC task can be contrasted with that of an animal moving within a natural environment. Animal sensory systems, such as vision and auditory sensing, have evolved over millions of years to detect, identify, and respond to novel events that could pose a threat or indicate a reward. As a result, when a new sound or sight is observed, most animals will make an immediate decision to classify it as friend, foe, or neutral. Animals perform this task without an explicit model or expert knowledge of the environment. Instead, they rely on previously learned low-level environmental features (such as edges and luminance transitions in vision) that generate activity in the different layers of neurons within the sensory cortex. As the information propagates through layers of the cortex, the concepts that the neurons are sensitive to become more and more abstract. Decisions based on these hierarchical features (referred to as receptive fields or weight values) are what allow the animal to make the friend-foe decision. This decision can be made without having prior knowledge of the exact input properties and in the presence of noise or corruption. Further, the process is naturally suited to non-cooperative environments.
In view of the above, it would be desirable to have a biologically inspired automatic classification method, device, and system that do not require advance expert knowledge or complicated models and that function well in a noisy, non-cooperative environment.